基于KFCM与改进CV模型的Split Bregman图像分割方法

Split Bregman Method of Image Segmentation Based on KFCM and Improved CV Model

针对基于梯度变化的水平集图像分割方法对噪声敏感、计算效率不高、分割结果依赖初始值等问题,提出了一种基于KFCM与改进CV模型的Split Bregman图像分割方法。该算法首先通过核模糊C均值的聚类方法确定出感兴趣区域作为分割初始值,然后采用Split Bregman方法来提高CV模型的迭代计算时间效率。实验结果表明,所提算法不仅保持了CV模型图像分割算法的优势,而且在抗噪性能和分割效率方面有明显效果。

Aiming at the problems of the sensitivity to noise,the low computational efficiency and the segmentation result depended on heavily the initial value,we put forward an image segmentation method based on KFCM and improved CV model.Firstly,the average nuclear fuzzy clustering method was used to determine the interest area as the initial value in the algorithm.Then we improved the efficiency of iterative calculation of the CV model in calculation and time by the Split Bregman method.The experimental results show that this algorithm not only keeps the advantage of image segmentation algorithm,but also has obvious effect on the noise performance and segmentation efficiency.